Postgraduate Programme and Module Handbook 2022-2023 (archived)
Module MATH43020: Stochastic Processes
Department: Mathematical Sciences
MATH43020: Stochastic Processes
| Type | Tied | Level | 4 | Credits | 20 | Availability | Available in 2022/23 | Module Cap | None. |
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| Tied to | G1K509 |
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Prerequisites
- Analysis in Many Variables and Markov Chains or Probability
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To develop models for processes evolving randomly in time, and probabilistic methods for their analysis, building on the treatment of probability at Levels 1 and 2. Students completing this course should be equipped to read for themselves much of the vast literature on applications of stochastic processes to problems in physics, engineering, chemistry, biology, medicine, psychology, and other fields.
Content
- Branching processes
- Poisson processes
- Continuous-time Markov chains
- Discrete-time martingales and their applications
- Discrete renewal theory
- Further topics chosen from: coupling; further applications of martingale theory; general renewal theory; queueing theory
Learning Outcomes
Subject-specific Knowledge:
- By the end of the module students will: be able to solve seen and unseen problems on the given topics.
- Have a knowledge and understanding of this subject demonstrated through an ability to compute probabilities of events associated with a variety of important stochastic processes, and to analyse the behaviour of such processes.
- Reproduce theoretical mathematics concerning stochastic processes at a level appropriate to Level 3, including key definitions and theorems.
Subject-specific Skills:
- In addition students will have enhanced mathematical skills in the following areas: modelling, computation.
Key Skills:
- Students will be able to study independently to further their knowledge of an advanced topic.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Subject material assigned for independent study develops the ability to acquire knowledge and understanding without dependence on lectures.
- Problem classes show how to solve example problems in an ideal way, revealing also the thought processes behind such solutions.
- Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
- The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 42 | 2 per week in Michaelmas and Epiphany and 2 in Easter | 1 hour | 42 | |
| Problems Classes | 8 | Fortnightly in Michaelmas and Epiphany | 1 hour | 8 | |
| Preparation and Reading | 150 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Written Examination | 3 hours | 100% | |
Formative Assessment:
Four assignments in each of the first two terms.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University